Tag: GPU Technology Conference

Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.

The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.

After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.

Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.

Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

It’s great to see the two leading teams in AI computing race while we collaborate deeply across the board – tuning TensorFlow performance, and accelerating the Google cloud with NVIDIA CUDA GPUs.

It provides a 5x improvement over Pascal, the current-generation NVIDIA GPU architecture, in peak teraflops, and 15x over the Maxwell architecture, launched just two years ago – well beyond what Moore’s law would have predicted.

Such leaps in performance have drawn innovators from every industry, with the number of startups building GPU-driven AI services growing more than 4x over the past year to 1,300.

To help innovators move seamlessly to cloud services such as these, at GTC we launched the NVIDIA GPU Cloud platform, which contains a registry of pre-configured and optimized stacks of every framework.

Computer giant Nvidia developed a convolutional neural network (CNN) to learn how to steer a car in any weather condition, using only the data taken from cameras and a car’s steering wheel.

“A small amount of training data from less than 100 hours of driving was sufficient to train the car to operate in diverse conditions, on highways, local and residential roads in sunny, cloudy and rainy conditions.”

By collecting data from roads in New Jersey, Nvidia was able to train the CNN to steer a car the same way a human does in the same conditions.

Artificial intelligence takes less than 100 hours to learn to drive-slower than a human.

Nvidia’s deep-learning algorithm can teach a car to drive itself in all weather conditions.